Automated Detection of Postoperative Surgical Site Infections Using Supervised Methods with Electronic Health Record Data

Zhen Hu, Gyorgy J. Simon, Elliot G. Arsoniadis, Yan Wang, Mary R. Kwaan, Genevieve B. Melton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Scopus citations

Abstract

The National Surgical Quality Improvement Project (NSQIP) is widely recognized as 'the best in the nation' surgical quality improvement resource in the United States. In particular, it rigorously defines postoperative morbidity outcomes, including surgical adverse events occurring within 30 days of surgery. Due to its manual yet expensive construction process, the NSQIP registry is of exceptionally high quality, but its high cost remains a significant bottleneck to NSQIP's wider dissemination. In this work, we propose an automated surgical adverse events detection tool, aimed at accelerating the process of extracting postoperative outcomes from medical charts. As a prototype system, we combined local EHR data with the NSQIP gold standard outcomes and developed machine learned models to retrospectively detect Surgical Site Infections (SSI), a particular family of adverse events that NSQIP extracts. The built models have high specificity (from 0.788 to 0.988) as well as very high negative predictive values (>0.98), reliably eliminating the vast majority of patients without SSI, thereby significantly reducing the NSQIP extractors' burden.

Original languageEnglish (US)
Title of host publicationMEDINFO 2015
Subtitle of host publicationeHealth-Enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics
EditorsAndrew Georgiou, Indra Neil Sarkar, Paulo Mazzoncini de Azevedo Marques
PublisherIOS Press
Pages706-710
Number of pages5
ISBN (Electronic)9781614995630
DOIs
StatePublished - 2015
Event15th World Congress on Health and Biomedical Informatics, MEDINFO 2015 - Sao Paulo, Brazil
Duration: Aug 19 2015Aug 23 2015

Publication series

NameStudies in Health Technology and Informatics
Volume216
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other15th World Congress on Health and Biomedical Informatics, MEDINFO 2015
CountryBrazil
CitySao Paulo
Period8/19/158/23/15

Keywords

  • Electronic Health Records
  • National Surgical Quality Improvement Project
  • Supervised Learning
  • Surgical Site Infection

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